Identifying objects in an image is performed in a variety of image processing functions. For example, in correcting for red-eye in images, the human eye is located and the undesirable red portion in the eye is replaced with a more aesthetically pleasing color. In the "KODAK" digital print station, the image is displayed on a touch screen and one eye is repeatedly touched for further zooming in on the red portion of the eye upon each touch. The red portion of the eye is then identified by searching for red pixels in the area defined by the zooming process, and the identified red pixels are replaced with a predetermined color for making the image more aesthetically pleasant. The process is then repeated for the other eye.
A neural networks method of locating human eyes is disclosed in Learning An Example Selection for Object and Pattern Recognition, The AI-Lab, MIT by K. K. Sung, November 1995. This method discloses training the a neural net to recognize eyes with acceptable distortion from a pre-selected eye template. The operator repeatedly distorts the original eye template and all variations produced from distorting the eye are labeled as either acceptable or unacceptable. The distorted samples, i.e., the training images, and the associated labeling information are fed to the neural net. This training process is repeated until the neural net has achieved satisfactory recognition performance for the training images. The trained neural net effectively has stored possible variations of the eye. Locating an eye is done by feeding a region in the image to the neural net for determining if a desired output, i.e., a match, occurs; all matches are identified as an eye.
Although the presently known and utilized methods of identifying eyes are satisfactory, they are not without drawbacks. The touch screen method requires constant human interaction of repeatedly touching the touch screen for zooming in on the eye and, as a result, is somewhat labor intensive. Still further, the neural net method requires extensive training and is also computationally intensive in the matching process because an exhaustive search has to be performed for all the possible sizes and orientations of the eye.
Consequently, a need exists for improvements in the method of locating objects in an image so as to overcome the above-described drawbacks.